Mode (SQL and notebooks for BI)
Mode is a collaborative analytics and BI platform that lets analysts query a warehouse with SQL, then explore and visualize results in notebooks (Python/R) and shareable reports. It targets analyst-driven, code-friendly analysis on top of warehouse data, sitting between raw SQL and self-serve dashboards. It reads from connected data sources; it does not collect data itself.
What this means
Mode connects to a warehouse or database and lets analysts write SQL, pass results into Python or R notebooks for further analysis, and assemble visualizations into reports that others can view. The workflow is code-first but collaborative, aimed at analysts rather than purely point-and-click users.
It reads from your connected sources; the data lives there, and Mode is the query, analysis, and sharing layer.
What to weigh
Mode suits teams with SQL and notebook skills who want flexible exploration plus sharing. For fully self-serve, point-and-click dashboards aimed at non-analysts, a different BI tool may fit better; many organizations use both for different audiences.
- SQL plus Python/R notebooks over a warehouse
- Analyst-centric, collaborative reporting
- Reads connected sources; does not collect data
Where it fits
It fits the analysis-and-reporting layer of a warehouse stack, after modeling. Report correctness depends on the SQL and any notebook logic, so consistent definitions (often in dbt) keep results aligned across reports.
How it appears in analytics and logs
Mode results reflect the SQL and notebook code run against connected sources; a wrong report usually traces to the query or model, not collection.
Diagnostic use case
Use Mode for analyst-led, code-friendly exploration and reporting over warehouse data, combining SQL with notebooks and shareable results.
What WebmasterID can help detect
WebmasterID is a first-party measurement tool; this page explains Mode's analyst-centric BI model so you can see how exported analytics data is explored and reported.
Common mistakes
- Expecting Mode to collect data rather than query sources.
- Using it for fully self-serve dashboards aimed at non-analysts.
- Letting ad-hoc SQL drift from shared metric definitions.
Privacy and accuracy notes
Mode queries data from sources you connect; what is exposed depends on those sources and access controls. This is factual, not legal advice.
Related pages
- Hex (collaborative data notebooks)
Hex is a collaborative data workspace built around notebooks that combine SQL, Python, and no-code cells, with the ability to publish results as interactive data apps. It targets analysts and data scientists working over warehouse data, blending exploratory analysis with shareable outputs. It reads from connected sources rather than collecting data itself.
- Metabase vs Superset (open-source BI)
Metabase and Apache Superset are both open-source business intelligence tools that query a warehouse and build dashboards, but they emphasize different users. Metabase leans toward approachable, ask-a-question exploration for non-technical users; Superset leans toward SQL, configuration, and a broad chart library for technical users. Both read connected sources; the choice is about audience and workflow, not a winner.
- dbt and the analytics stack
dbt (data build tool) is a transformation framework that runs SQL SELECT statements as version-controlled models inside your data warehouse, turning raw loaded tables into clean, documented, tested datasets. It handles the 'T' in ELT — it does not move data in or visualize it. It adds software-engineering practices (testing, lineage, docs) to analytics SQL.
- Web analytics
First-party web measurement overview.
Sources and verification notes
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.